Python Data Analysis for Healthcare - Note 4

Author

SEOYEON CHOI

Published

June 5, 2025

1

import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
df_geo = pd.read_csv("../../../delete/Demo Health Facilities Geo Data_NHC.csv")
df_geo
Facility Type Latitude Longitude
0 Hospital 5.593051 3.697007
1 Hospital 10.017569 9.921846
2 Hospital 7.568808 11.178651
3 Hospital 11.574567 12.594135
4 Hospital 6.093634 9.334975
... ... ... ...
762 Pharmacy 11.844224 9.831699
763 Pharmacy 8.228317 8.502529
764 Pharmacy 8.162100 4.821424
765 Pharmacy 7.568452 8.388384
766 Pharmacy 4.330299 10.229440

767 rows × 3 columns

df_geo['Facility Type'].value_counts()
Pharmacy                         627
Primary Care Center              118
Specialized Healthcare Center     12
Hospital                          10
Name: Facility Type, dtype: int64

기관 summary 보는 법

plt.figure(figsize=(8,4))
df_geo['Facility Type'].value_counts().plot(kind='bar')
plt.xlabel("Facility Type")
plt.ylabel("Count")
plt.xticks(rotation=45)
plt.title("Public Health Facility Types")
plt.show()

약국이 제일 많음

primary care center 는 기본 진료 기관?

gdf = gpd.GeoDataFrame(df_geo,geometry=gpd.points_from_xy(df_geo.Longitude, df_geo.Latitude))
gdf.head()
Facility Type Latitude Longitude geometry
0 Hospital 5.593051 3.697007 POINT (3.69701 5.59305)
1 Hospital 10.017569 9.921846 POINT (9.92185 10.01757)
2 Hospital 7.568808 11.178651 POINT (11.17865 7.56881)
3 Hospital 11.574567 12.594135 POINT (12.59413 11.57457)
4 Hospital 6.093634 9.334975 POINT (9.33497 6.09363) 
gdf.plot()

gdf.plot(markersize=10,color='blue', alpha=0.5)
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Health Facility Type')
plt.show()

지도 안 깔고 plot으로만 봐보면 이럼

2

##Explore Public Health Facilities Geolocation Demo Dataset using Geopandas Part I

import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
df_geo = pd.read_csv("/content/demo_health_facilities_geo_data.csv")
df_geo
Facility Type Latitude Longitude
0 Hospital 5.593051 3.697007
1 Hospital 10.017569 9.921846
2 Hospital 7.568808 11.178651
3 Hospital 11.574567 12.594135
4 Hospital 6.093634 9.334975
... ... ... ...
762 Pharmacy 11.844224 9.831699
763 Pharmacy 8.228317 8.502529
764 Pharmacy 8.162100 4.821424
765 Pharmacy 7.568452 8.388384
766 Pharmacy 4.330299 10.229440

767 rows × 3 columns

df_geo['Facility Type'].value_counts()
Pharmacy                         627
Primary Care Center              118
Specialized Healthcare Center     12
Hospital                          10
Name: Facility Type, dtype: int64
plt.figure(figsize=(8,4))
df_geo['Facility Type'].value_counts().plot(kind='bar')
plt.xlabel("Facility Type")
plt.ylabel("Count")
plt.xticks(rotation=45)
plt.title("Public Health Facility Types")
plt.show()

gdf = gpd.GeoDataFrame(df_geo,geometry=gpd.points_from_xy(df_geo.Longitude, df_geo.Latitude))
gdf
Facility Type Latitude Longitude geometry
0 Hospital 5.593051 3.697007 POINT (3.69701 5.59305)
1 Hospital 10.017569 9.921846 POINT (9.92185 10.01757)
2 Hospital 7.568808 11.178651 POINT (11.17865 7.56881)
3 Hospital 11.574567 12.594135 POINT (12.59413 11.57457)
4 Hospital 6.093634 9.334975 POINT (9.33497 6.09363)
... ... ... ... ...
762 Pharmacy 11.844224 9.831699 POINT (9.83170 11.84422)
763 Pharmacy 8.228317 8.502529 POINT (8.50253 8.22832)
764 Pharmacy 8.162100 4.821424 POINT (4.82142 8.16210)
765 Pharmacy 7.568452 8.388384 POINT (8.38838 7.56845)
766 Pharmacy 4.330299 10.229440 POINT (10.22944 4.33030)

767 rows × 4 columns

gdf.plot()

gdf.plot(markersize=10,color='blue', alpha=0.5)
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Health Facility Type')
plt.show()

##Explore Public Health Facilities Geolocation Demo Dataset using Geopandas Part II

buffers = gdf.copy()
buffers['geometry'] = gdf.buffer(0.10)
buffers.plot()

buffers.plot(color='blue', edgecolor='k', alpha=0.8)

gdf.plot(color='blue', markersize=10, label="Geopoints")
buffers.plot(edgecolor="red", linewidth=2,label="Buffers", alpha=0.8)
plt.title("Geopoints and Buffer")
plt.show()

fig, ax = plt.subplots(figsize=(10,8))
gdf.plot(ax=ax,color='blue', markersize=4,label="Geopoints")
buffers.plot(ax=ax, edgecolor='red', linewidth=1,label='Buffers')
#plt.legend()
plt.title("Geopoints and Buffer")
plt.show()

3

import pandas as pd
import folium
df_geo = pd.read_csv('/content/demo_health_facilities_geo_data.csv')
df_geo
Facility Type Latitude Longitude
0 Hospital 5.593051 3.697007
1 Hospital 10.017569 9.921846
2 Hospital 7.568808 11.178651
3 Hospital 11.574567 12.594135
4 Hospital 6.093634 9.334975
... ... ... ...
762 Pharmacy 11.844224 9.831699
763 Pharmacy 8.228317 8.502529
764 Pharmacy 8.162100 4.821424
765 Pharmacy 7.568452 8.388384
766 Pharmacy 4.330299 10.229440

767 rows × 3 columns

df_geo['Facility Type'].nunique()
4
df_geo['Facility Type'].unique()
array(['Hospital', 'Primary Care Center', 'Specialized Healthcare Center',
       'Pharmacy'], dtype=object)
m = folium.Map(location=[df_geo['Latitude'].iloc[0], df_geo['Longitude'][0]])
for index,row in df_geo.iterrows():
    folium.Marker([row['Latitude'],row['Longitude']], popup=row['Facility Type']).add_to(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook
facility_color_mapping = {'Hospital':'red', 'Primary Care Center':'blue','Specialized Healthcare Center':'green','Pharmacy':'purple'}
m = folium.Map(location=[df_geo['Latitude'].iloc[0], df_geo['Longitude'][0]])
for index, row in df_geo.iterrows():
    facility_type = row['Facility Type']
    if facility_type in facility_color_mapping:
        color = facility_color_mapping[facility_type]
    else:
        color = 'gray'
    folium.Marker(location=[row['Latitude'],row['Longitude']],popup=row['Facility Type'], icon=folium.Icon(color=color)).add_to(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook

4

##Interactive Map for Public Health Facilities Geolocation Demo Dataset using Folium Part I

import pandas as pd
import folium
df_geo = pd.read_csv('/content/demo_health_facilities_geo_data.csv')
df_geo
Facility Type Latitude Longitude
0 Hospital 5.593051 3.697007
1 Hospital 10.017569 9.921846
2 Hospital 7.568808 11.178651
3 Hospital 11.574567 12.594135
4 Hospital 6.093634 9.334975
... ... ... ...
762 Pharmacy 11.844224 9.831699
763 Pharmacy 8.228317 8.502529
764 Pharmacy 8.162100 4.821424
765 Pharmacy 7.568452 8.388384
766 Pharmacy 4.330299 10.229440

767 rows × 3 columns

df_geo['Facility Type'].nunique()
4
df_geo['Facility Type'].unique()
array(['Hospital', 'Primary Care Center', 'Specialized Healthcare Center',
       'Pharmacy'], dtype=object)
m = folium.Map(location=[df_geo['Latitude'].iloc[0], df_geo['Longitude'][0]])
for index,row in df_geo.iterrows():
    folium.Marker([row['Latitude'],row['Longitude']], popup=row['Facility Type']).add_to(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook
facility_color_mapping = {'Hospital':'red', 'Primary Care Center':'blue','Specialized Healthcare Center':'green','Pharmacy':'purple'}
m = folium.Map(location=[df_geo['Latitude'].iloc[0], df_geo['Longitude'][0]])
for index, row in df_geo.iterrows():
    facility_type = row['Facility Type']
    if facility_type in facility_color_mapping:
        color = facility_color_mapping[facility_type]
    else:
        color = 'gray'
    folium.Marker(location=[row['Latitude'],row['Longitude']],popup=row['Facility Type'], icon=folium.Icon(color=color)).add_to(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook

##Interactive Map for Public Health Facilities Geolocation Demo Dataset using Folium Part II

m = folium.Map(location=[df_geo['Latitude'].iloc[0], df_geo['Longitude'].iloc[0]])
facility_types_to_visualize = ['Hospital']
filtered_df = df_geo[df_geo['Facility Type'].isin(facility_types_to_visualize)]

for index, row in filtered_df.iterrows():
    facility_type = row['Facility Type']
    color = facility_color_mapping.get(facility_type,'gray')
    folium.Marker(location=[row['Latitude'],row['Longitude']], popup=row['Facility Type'], icon=folium.Icon(color=color)).add_to(m)

m
Make this Notebook Trusted to load map: File -> Trust Notebook

5

##Interactive Map for Public Health Facilities Geolocation Demo Dataset using Folium Part I

import pandas as pd
import folium
df_geo = pd.read_csv('/content/demo_health_facilities_geo_data.csv')
df_geo
Facility Type Latitude Longitude
0 Hospital 5.593051 3.697007
1 Hospital 10.017569 9.921846
2 Hospital 7.568808 11.178651
3 Hospital 11.574567 12.594135
4 Hospital 6.093634 9.334975
... ... ... ...
762 Pharmacy 11.844224 9.831699
763 Pharmacy 8.228317 8.502529
764 Pharmacy 8.162100 4.821424
765 Pharmacy 7.568452 8.388384
766 Pharmacy 4.330299 10.229440

767 rows × 3 columns

df_geo['Facility Type'].nunique()
4
df_geo['Facility Type'].unique()
array(['Hospital', 'Primary Care Center', 'Specialized Healthcare Center',
       'Pharmacy'], dtype=object)
m = folium.Map(location=[df_geo['Latitude'].iloc[0], df_geo['Longitude'][0]])
for index,row in df_geo.iterrows():
    folium.Marker([row['Latitude'],row['Longitude']], popup=row['Facility Type']).add_to(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook
facility_color_mapping = {'Hospital':'red', 'Primary Care Center':'blue','Specialized Healthcare Center':'green','Pharmacy':'purple'}
m = folium.Map(location=[df_geo['Latitude'].iloc[0], df_geo['Longitude'][0]])
for index, row in df_geo.iterrows():
    facility_type = row['Facility Type']
    if facility_type in facility_color_mapping:
        color = facility_color_mapping[facility_type]
    else:
        color = 'gray'
    folium.Marker(location=[row['Latitude'],row['Longitude']],popup=row['Facility Type'], icon=folium.Icon(color=color)).add_to(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook

##Interactive Map for Public Health Facilities Geolocation Demo Dataset using Folium Part II

m = folium.Map(location=[df_geo['Latitude'].iloc[0], df_geo['Longitude'].iloc[0]])
facility_types_to_visualize = ['Hospital']
filtered_df = df_geo[df_geo['Facility Type'].isin(facility_types_to_visualize)]

for index, row in filtered_df.iterrows():
    facility_type = row['Facility Type']
    color = facility_color_mapping.get(facility_type,'gray')
    folium.Marker(location=[row['Latitude'],row['Longitude']], popup=row['Facility Type'], icon=folium.Icon(color=color)).add_to(m)

m
Make this Notebook Trusted to load map: File -> Trust Notebook

##Interactive Map for Public Health Facilities Geolocation Demo Dataset using Folium Part III

from folium.plugins import HeatMap
m = folium.Map(location=[df_geo['Latitude'].iloc[0],df_geo['Longitude'].iloc[0]])
heat_data = [[row['Latitude'], row['Longitude']] for index, row in df_geo.iterrows()]
HeatMap(heat_data).add_to(m)

m
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m.save('facility_heatmap.html')
from folium.plugins import MarkerCluster
m = folium.Map(location=[df_geo['Latitude'].iloc[0],df_geo['Longitude'].iloc[0]])
marker_cluster = MarkerCluster()
for index, row in df_geo.iterrows():
    marker = folium.Marker([row['Latitude'],row['Longitude']], popup=row['Facility Type'])
    marker.add_to(marker_cluster)

marker_cluster.add_to(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook